Overview

Dataset statistics

Number of variables11
Number of observations10908300
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory915.5 MiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

EngineSpeed is highly correlated with EngineAirInletPressure and 1 other fieldsHigh correlation
Fuel Rate is highly correlated with Engine Load and 2 other fieldsHigh correlation
Engine Load is highly correlated with Boost Pressure and 2 other fieldsHigh correlation
Boost Pressure is highly correlated with Engine Load and 2 other fieldsHigh correlation
EngineAirInletPressure is highly correlated with EngineSpeed and 3 other fieldsHigh correlation
AcceleratorPedalPos is highly correlated with Engine Load and 2 other fieldsHigh correlation
VehicleSpeed is highly correlated with EngineSpeedHigh correlation
Fuel Rate is highly skewed (γ1 = 43.53356777) Skewed
Timestamp has unique values Unique
LongitudAcc has 2127170 (19.5%) zeros Zeros
EngineSpeed has 231436 (2.1%) zeros Zeros
Fuel Rate has 2554907 (23.4%) zeros Zeros
Engine Load has 2567256 (23.5%) zeros Zeros
Boost Pressure has 1653306 (15.2%) zeros Zeros
AcceleratorPedalPos has 4389455 (40.2%) zeros Zeros
VehicleSpeed has 1466420 (13.4%) zeros Zeros
BrakePedalPos has 8997303 (82.5%) zeros Zeros

Reproduction

Analysis started2022-11-22 21:23:49.537100
Analysis finished2022-11-22 21:37:20.893759
Duration13 minutes and 31.36 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Timestamp
Real number (ℝ≥0)

UNIQUE

Distinct10908300
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8106894 × 1010
Minimum2.627644776 × 1010
Maximum8.299465088 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.2 MiB
2022-11-22T22:37:21.090604image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2.627644776 × 1010
5-th percentile2.840024754 × 1010
Q15.040331817 × 1010
median6.059781273 × 1010
Q37.260733137 × 1010
95-th percentile8.109723985 × 1010
Maximum8.299465088 × 1010
Range5.671820312 × 1010
Interquartile range (IQR)2.22040132 × 1010

Descriptive statistics

Standard deviation1.728947594 × 1010
Coefficient of variation (CV)0.2975460355
Kurtosis-1.027714024
Mean5.8106894 × 1010
Median Absolute Deviation (MAD)1.184510022 × 1010
Skewness-0.4739254843
Sum6.338474318 × 1017
Variance2.989259784 × 1020
MonotonicityStrictly increasing
2022-11-22T22:37:21.222160image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.627644776 × 10101
 
< 0.1%
6.955835094 × 10101
 
< 0.1%
6.955835293 × 10101
 
< 0.1%
6.955835493 × 10101
 
< 0.1%
6.955835596 × 10101
 
< 0.1%
6.955835686 × 10101
 
< 0.1%
6.955835794 × 10101
 
< 0.1%
6.955835986 × 10101
 
< 0.1%
6.955836094 × 10101
 
< 0.1%
6.955836194 × 10101
 
< 0.1%
Other values (10908290)10908290
> 99.9%
ValueCountFrequency (%)
2.627644776 × 10101
< 0.1%
2.627644846 × 10101
< 0.1%
2.627644955 × 10101
< 0.1%
2.627645072 × 10101
< 0.1%
2.627645144 × 10101
< 0.1%
2.627645262 × 10101
< 0.1%
2.627645376 × 10101
< 0.1%
2.627645445 × 10101
< 0.1%
2.627645562 × 10101
< 0.1%
2.62764568 × 10101
< 0.1%
ValueCountFrequency (%)
8.299465088 × 10101
< 0.1%
8.299464979 × 10101
< 0.1%
8.299464867 × 10101
< 0.1%
8.299464799 × 10101
< 0.1%
8.299464682 × 10101
< 0.1%
8.299464567 × 10101
< 0.1%
8.299464499 × 10101
< 0.1%
8.29946438 × 10101
< 0.1%
8.299464266 × 10101
< 0.1%
8.299464193 × 10101
< 0.1%

WetTankAirPressure
Real number (ℝ≥0)

Distinct181
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.0609771
Minimum0
Maximum12.411
Zeros2243
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size83.2 MiB
2022-11-22T22:37:21.371785image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.2046
Q110.82515
median11.1699
Q311.51465
95-th percentile11.79045
Maximum12.411
Range12.411
Interquartile range (IQR)0.6895

Descriptive statistics

Standard deviation0.7514544128
Coefficient of variation (CV)0.06793743502
Kurtosis42.85687793
Mean11.0609771
Median Absolute Deviation (MAD)0.34475
Skewness-5.007766149
Sum120656456.5
Variance0.5646837345
MonotonicityNot monotonic
2022-11-22T22:37:21.531971image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.51465652909
 
6.0%
11.4457651738
 
6.0%
11.5836612920
 
5.6%
11.37675612235
 
5.6%
10.8941587552
 
5.4%
10.82515579800
 
5.3%
10.96305566822
 
5.2%
11.23885556980
 
5.1%
11.1699543814
 
5.0%
11.10095536611
 
4.9%
Other values (171)5006919
45.9%
ValueCountFrequency (%)
02243
< 0.1%
0.0689517
 
< 0.1%
0.137916
 
< 0.1%
0.2068513
 
< 0.1%
0.275811
 
< 0.1%
0.3447517
 
< 0.1%
0.413712
 
< 0.1%
0.4826518
 
< 0.1%
0.551646
 
< 0.1%
0.6205518
 
< 0.1%
ValueCountFrequency (%)
12.41113
 
< 0.1%
12.3420538
 
< 0.1%
12.2731116
 
< 0.1%
12.20415223
 
< 0.1%
12.13521407
 
< 0.1%
12.066259893
 
0.1%
11.997344284
 
0.4%
11.9283593494
 
0.9%
11.8594215173
2.0%
11.79045325162
3.0%

LongitudAcc
Real number (ℝ)

ZEROS

Distinct120
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03205364722
Minimum-10.1
Maximum13
Zeros2127170
Zeros (%)19.5%
Negative4605319
Negative (%)42.2%
Memory size83.2 MiB
2022-11-22T22:37:21.673279image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-10.1
5-th percentile-1.1
Q1-0.3
median0
Q30.3
95-th percentile0.9
Maximum13
Range23.1
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.6739510985
Coefficient of variation (CV)-21.02572272
Kurtosis90.5194209
Mean-0.03205364722
Median Absolute Deviation (MAD)0.3
Skewness4.412395045
Sum-349650.8
Variance0.4542100832
MonotonicityNot monotonic
2022-11-22T22:37:21.810256image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02127170
19.5%
-0.1735450
 
6.7%
-0.2691634
 
6.3%
0.1684007
 
6.3%
0.2610760
 
5.6%
-0.3591412
 
5.4%
0.3537225
 
4.9%
-0.4504214
 
4.6%
0.4461032
 
4.2%
0.5396778
 
3.6%
Other values (110)3568618
32.7%
ValueCountFrequency (%)
-10.11
< 0.1%
-9.71
< 0.1%
-8.71
< 0.1%
-7.62
< 0.1%
-7.12
< 0.1%
-6.81
< 0.1%
-6.71
< 0.1%
-6.51
< 0.1%
-6.42
< 0.1%
-6.31
< 0.1%
ValueCountFrequency (%)
133402
< 0.1%
12.93801
< 0.1%
5.41
 
< 0.1%
5.11
 
< 0.1%
4.81
 
< 0.1%
4.72
 
< 0.1%
4.51
 
< 0.1%
4.44
 
< 0.1%
4.32
 
< 0.1%
4.25
 
< 0.1%

EngineSpeed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14429
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1073.753356
Minimum0
Maximum8191.875
Zeros231436
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size83.2 MiB
2022-11-22T22:37:21.959466image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile549.5
Q1933.625
median1163.125
Q31286.625
95-th percentile1447.875
Maximum8191.875
Range8191.875
Interquartile range (IQR)353

Descriptive statistics

Standard deviation326.1297299
Coefficient of variation (CV)0.3037287177
Kurtosis9.127563814
Mean1073.753356
Median Absolute Deviation (MAD)149
Skewness-0.640048706
Sum1.171282373 × 1010
Variance106360.6008
MonotonicityNot monotonic
2022-11-22T22:37:22.091718image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0231436
 
2.1%
599.87510276
 
0.1%
600.510273
 
0.1%
600.37510199
 
0.1%
60010182
 
0.1%
600.12510172
 
0.1%
600.7510161
 
0.1%
600.2510139
 
0.1%
599.7510100
 
0.1%
600.87510094
 
0.1%
Other values (14419)10585268
97.0%
ValueCountFrequency (%)
0231436
2.1%
15.6251
 
< 0.1%
16.51
 
< 0.1%
17.251
 
< 0.1%
17.3751
 
< 0.1%
19.1251
 
< 0.1%
20.252
 
< 0.1%
20.8751
 
< 0.1%
21.751
 
< 0.1%
21.8751
 
< 0.1%
ValueCountFrequency (%)
8191.875395
< 0.1%
2151.51
 
< 0.1%
2138.6251
 
< 0.1%
2133.3751
 
< 0.1%
2132.6251
 
< 0.1%
2126.1252
 
< 0.1%
2125.6252
 
< 0.1%
2124.751
 
< 0.1%
21241
 
< 0.1%
2123.751
 
< 0.1%

Fuel Rate
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.72080786
Minimum0
Maximum3876.198645
Zeros2554907
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size83.2 MiB
2022-11-22T22:37:22.234954image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.064646
median8.576315
Q321.766096
95-th percentile46.312101
Maximum3876.198645
Range3876.198645
Interquartile range (IQR)20.70145

Descriptive statistics

Standard deviation85.92078612
Coefficient of variation (CV)5.465417993
Kurtosis1952.6881
Mean15.72080786
Median Absolute Deviation (MAD)8.576315
Skewness43.53356777
Sum171487288.4
Variance7382.381488
MonotonicityNot monotonic
2022-11-22T22:37:22.373097image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02554907
 
23.4%
3.31223265396
 
0.6%
3.37137963773
 
0.6%
3.25308561747
 
0.6%
3.43052660585
 
0.6%
3.48967356004
 
0.5%
3.19393855414
 
0.5%
3.5488252889
 
0.5%
3.96284952520
 
0.5%
4.02199652294
 
0.5%
Other values (1098)7832771
71.8%
ValueCountFrequency (%)
02554907
23.4%
0.05914710599
 
0.1%
0.11829410312
 
0.1%
0.17744111789
 
0.1%
0.23658814361
 
0.1%
0.29573513892
 
0.1%
0.35488212336
 
0.1%
0.41402911726
 
0.1%
0.47317610366
 
0.1%
0.5323238726
 
0.1%
ValueCountFrequency (%)
3876.1986455231
< 0.1%
3870.2247981
 
< 0.1%
3860.8795721
 
< 0.1%
3783.2787081
 
< 0.1%
3025.1324621
 
< 0.1%
2840.1797931
 
< 0.1%
2004.7875651
 
< 0.1%
65.061724
 
< 0.1%
65.00255322
 
< 0.1%
64.94340638
 
< 0.1%

Engine Load
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.36381581
Minimum0
Maximum100
Zeros2567256
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size83.2 MiB
2022-11-22T22:37:22.663678image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median24.5
Q345.5
95-th percentile89.5
Maximum100
Range100
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation27.65931726
Coefficient of variation (CV)0.9109302148
Kurtosis-0.0496729248
Mean30.36381581
Median Absolute Deviation (MAD)21
Skewness0.8495695205
Sum331217612
Variance765.0378312
MonotonicityNot monotonic
2022-11-22T22:37:22.803159image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02567256
 
23.5%
100287812
 
2.6%
21125412
 
1.1%
20.5125030
 
1.1%
21.5124726
 
1.1%
22.5124092
 
1.1%
23122595
 
1.1%
22122413
 
1.1%
23.5121785
 
1.1%
20119480
 
1.1%
Other values (191)7067699
64.8%
ValueCountFrequency (%)
02567256
23.5%
0.542708
 
0.4%
133287
 
0.3%
1.526477
 
0.2%
224737
 
0.2%
2.521951
 
0.2%
323528
 
0.2%
3.522103
 
0.2%
424212
 
0.2%
4.522611
 
0.2%
ValueCountFrequency (%)
100287812
2.6%
99.59808
 
0.1%
9911572
 
0.1%
98.514015
 
0.1%
9812458
 
0.1%
97.512369
 
0.1%
9711842
 
0.1%
96.511591
 
0.1%
9612062
 
0.1%
95.511411
 
0.1%

Boost Pressure
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct188
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2223742027
Minimum0
Maximum1.611566
Zeros1653306
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size83.2 MiB
2022-11-22T22:37:22.945803image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.051708
median0.120652
Q30.30163
95-th percentile0.784238
Maximum1.611566
Range1.611566
Interquartile range (IQR)0.249922

Descriptive statistics

Standard deviation0.2616009084
Coefficient of variation (CV)1.176399534
Kurtosis4.157642348
Mean0.2223742027
Median Absolute Deviation (MAD)0.112034
Skewness1.962960112
Sum2425724.515
Variance0.06843503528
MonotonicityNot monotonic
2022-11-22T22:37:23.083960image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01653306
 
15.2%
0.08618436050
 
4.0%
0.077562428160
 
3.9%
0.094798396454
 
3.6%
0.068944370214
 
3.4%
0.103416334827
 
3.1%
0.060326288077
 
2.6%
0.112034270791
 
2.5%
0.051708228753
 
2.1%
0.120652223067
 
2.0%
Other values (178)6278601
57.6%
ValueCountFrequency (%)
01653306
15.2%
0.008618212132
 
1.9%
0.017236158043
 
1.4%
0.025854160661
 
1.5%
0.034472181503
 
1.7%
0.04309199324
 
1.8%
0.051708228753
 
2.1%
0.060326288077
 
2.6%
0.068944370214
 
3.4%
0.077562428160
 
3.9%
ValueCountFrequency (%)
1.6115663
 
< 0.1%
1.60294817
 
< 0.1%
1.5943315
 
< 0.1%
1.58571212
 
< 0.1%
1.57709415
 
< 0.1%
1.56847638
< 0.1%
1.55985825
 
< 0.1%
1.5512429
 
< 0.1%
1.54262268
< 0.1%
1.53400476
< 0.1%

EngineAirInletPressure
Real number (ℝ≥0)

HIGH CORRELATION

Distinct98
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.7234423
Minimum32
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.2 MiB
2022-11-22T22:37:23.236722image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile100
Q1106
median114
Q3132
95-th percentile180
Maximum510
Range478
Interquartile range (IQR)26

Descriptive statistics

Standard deviation26.37856635
Coefficient of variation (CV)0.2132058877
Kurtosis5.683933293
Mean123.7234423
Median Absolute Deviation (MAD)10
Skewness2.029374556
Sum1349612426
Variance695.8287627
MonotonicityNot monotonic
2022-11-22T22:37:23.379972image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110958940
 
8.8%
102920858
 
8.4%
100766118
 
7.0%
112754953
 
6.9%
108745740
 
6.8%
114509557
 
4.7%
106487704
 
4.5%
104434920
 
4.0%
116408424
 
3.7%
118349066
 
3.2%
Other values (88)4572020
41.9%
ValueCountFrequency (%)
323
 
< 0.1%
3433
< 0.1%
5015
< 0.1%
5215
< 0.1%
641
 
< 0.1%
6618
< 0.1%
6832
< 0.1%
704
 
< 0.1%
864
 
< 0.1%
921
 
< 0.1%
ValueCountFrequency (%)
510384
 
< 0.1%
50825
 
< 0.1%
2641
 
< 0.1%
26230
 
< 0.1%
26039
 
< 0.1%
25879
 
< 0.1%
256106
 
< 0.1%
254261
 
< 0.1%
252563
< 0.1%
2501074
< 0.1%

AcceleratorPedalPos
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct251
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.03600088
Minimum0
Maximum100
Zeros4389455
Zeros (%)40.2%
Negative0
Negative (%)0.0%
Memory size83.2 MiB
2022-11-22T22:37:23.529740image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40.4
Q366.8
95-th percentile97.6
Maximum100
Range100
Interquartile range (IQR)66.8

Descriptive statistics

Standard deviation35.03854349
Coefficient of variation (CV)0.9460671417
Kurtosis-1.427659708
Mean37.03600088
Median Absolute Deviation (MAD)40.4
Skewness0.2296627301
Sum403999808.4
Variance1227.69953
MonotonicityNot monotonic
2022-11-22T22:37:23.669493image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04389455
40.2%
100476280
 
4.4%
6255335
 
0.5%
60.853298
 
0.5%
59.653060
 
0.5%
59.252990
 
0.5%
62.851960
 
0.5%
64.451632
 
0.5%
57.651280
 
0.5%
61.251143
 
0.5%
Other values (241)5621867
51.5%
ValueCountFrequency (%)
04389455
40.2%
0.44766
 
< 0.1%
0.84753
 
< 0.1%
1.24801
 
< 0.1%
1.64513
 
< 0.1%
24587
 
< 0.1%
2.44475
 
< 0.1%
2.84715
 
< 0.1%
3.24972
 
< 0.1%
3.64476
 
< 0.1%
ValueCountFrequency (%)
100476280
4.4%
99.612331
 
0.1%
99.212148
 
0.1%
98.812924
 
0.1%
98.412157
 
0.1%
9812995
 
0.1%
97.612972
 
0.1%
97.213883
 
0.1%
96.813390
 
0.1%
96.414260
 
0.1%

VehicleSpeed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24448
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.4453664
Minimum0
Maximum255.97971
Zeros1466420
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size83.2 MiB
2022-11-22T22:37:23.807962image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.08478
median42.020748
Q360.355512
95-th percentile77.245056
Maximum255.97971
Range255.97971
Interquartile range (IQR)42.270732

Descriptive statistics

Standard deviation25.3683214
Coefficient of variation (CV)0.6431255103
Kurtosis-0.6116311278
Mean39.4453664
Median Absolute Deviation (MAD)20.91663
Skewness-0.04945009513
Sum430281890.3
Variance643.5517305
MonotonicityNot monotonic
2022-11-22T22:37:23.997654image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01466420
 
13.4%
69.0424561104
 
< 0.1%
69.0033961075
 
< 0.1%
69.0112081051
 
< 0.1%
68.9838661037
 
< 0.1%
69.1127641034
 
< 0.1%
70.0228621034
 
< 0.1%
68.9643361030
 
< 0.1%
69.1244821029
 
< 0.1%
68.9448061028
 
< 0.1%
Other values (24438)9432458
86.5%
ValueCountFrequency (%)
01466420
13.4%
0.999936111
 
< 0.1%
1.003842114
 
< 0.1%
1.00774897
 
< 0.1%
1.011654122
 
< 0.1%
1.01556112
 
< 0.1%
1.019466122
 
< 0.1%
1.023372122
 
< 0.1%
1.027278106
 
< 0.1%
1.031184104
 
< 0.1%
ValueCountFrequency (%)
255.97971392
< 0.1%
255.975804701
< 0.1%
106.8720661
 
< 0.1%
106.711921
 
< 0.1%
106.5322441
 
< 0.1%
106.3720981
 
< 0.1%
106.340851
 
< 0.1%
106.2900721
 
< 0.1%
106.282261
 
< 0.1%
105.6104281
 
< 0.1%

BrakePedalPos
Real number (ℝ≥0)

ZEROS

Distinct239
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.195319747
Minimum0
Maximum98
Zeros8997303
Zeros (%)82.5%
Negative0
Negative (%)0.0%
Memory size83.2 MiB
2022-11-22T22:37:24.157809image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21.6
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.538444664
Coefficient of variation (CV)2.359214495
Kurtosis4.886594089
Mean3.195319747
Median Absolute Deviation (MAD)0
Skewness2.31437582
Sum34855506.4
Variance56.82814796
MonotonicityNot monotonic
2022-11-22T22:37:24.295947image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08997303
82.5%
15.6100349
 
0.9%
17.297817
 
0.9%
1683924
 
0.8%
16.481978
 
0.8%
16.874605
 
0.7%
17.673225
 
0.7%
15.272021
 
0.7%
19.242756
 
0.4%
1840508
 
0.4%
Other values (229)1243814
 
11.4%
ValueCountFrequency (%)
08997303
82.5%
0.412071
 
0.1%
0.88297
 
0.1%
1.27850
 
0.1%
1.67856
 
0.1%
27415
 
0.1%
2.47218
 
0.1%
2.86913
 
0.1%
3.26504
 
0.1%
3.66708
 
0.1%
ValueCountFrequency (%)
9814
< 0.1%
97.631
< 0.1%
97.22
 
< 0.1%
96.41
 
< 0.1%
961
 
< 0.1%
95.61
 
< 0.1%
95.21
 
< 0.1%
94.84
 
< 0.1%
93.62
 
< 0.1%
93.22
 
< 0.1%

Interactions

2022-11-22T22:36:35.378952image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-22T22:33:06.445340image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:33:32.483176image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:33:57.925811image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:34:22.970904image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:34:49.866369image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:35:16.535056image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:35:42.775467image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:36:10.034077image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:36:37.682889image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-22T22:33:04.085905image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:33:30.094373image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:33:55.741302image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:34:20.521311image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:34:47.439046image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:35:14.173786image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:35:40.134931image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:36:07.745874image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-22T22:36:33.102666image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2022-11-22T22:37:24.432308image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-22T22:37:24.671561image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-22T22:37:24.917714image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-22T22:37:25.157446image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-22T22:37:25.399504image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-22T22:36:58.746089image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-22T22:37:03.459537image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TimestampWetTankAirPressureLongitudAccEngineSpeedFuel RateEngine LoadBoost PressureEngineAirInletPressureAcceleratorPedalPosVehicleSpeedBrakePedalPos
02.627645e+104.27490.00.00.00.00.0100.00.00.00.0
12.627645e+104.27490.00.00.00.00.0100.00.00.00.0
22.627645e+104.27490.00.00.00.00.0100.00.00.00.0
32.627645e+104.27490.00.00.00.00.0100.00.00.00.0
42.627645e+104.27490.00.00.00.00.0100.00.00.00.0
52.627645e+104.27490.00.00.00.00.0100.00.00.00.0
62.627645e+104.27490.00.00.00.00.0100.00.00.00.0
72.627645e+104.27490.00.00.00.00.0100.00.00.00.0
82.627646e+104.27490.00.00.00.00.0100.00.00.00.0
92.627646e+104.27490.00.00.00.00.0100.00.00.00.0

Last rows

TimestampWetTankAirPressureLongitudAccEngineSpeedFuel RateEngine LoadBoost PressureEngineAirInletPressureAcceleratorPedalPosVehicleSpeedBrakePedalPos
109082908.299464e+1011.79045-0.31126.1253.5488208.50.068944108.034.412.9835440.0
109082918.299464e+1011.79045-1.11079.6253.4896738.00.060326108.016.812.4015500.0
109082928.299464e+1011.79045-0.7952.7500.0000000.00.060326108.00.011.13991217.6
109082938.299464e+1011.72150-0.8684.2501.8927048.00.051708106.00.08.32759216.4
109082948.299465e+1011.72150-1.8566.7503.54882022.00.034472104.00.05.41762213.6
109082958.299465e+1011.65255-0.2596.5004.08114324.00.008618102.00.02.3318820.0
109082968.299465e+1011.58360-0.4616.7503.43052619.50.000000102.00.01.79676011.2
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